Crash Risk Analysis During Fog Conditions Using Real-Time Traffic Data

Keywords

Fog; Logistic regression; Ramps; Real-time crash risk; Real-Time traffic flow data; Real-time weather data

Abstract

This research investigates the changes of traffic characteristics and crash risks during fog conditions. Using real-time traffic flow and weather data at two regions in Florida, the traffic patterns at the fog duration were compared to the traffic patterns at the clear duration. It was found that the average 5-min speed and the average 5-min volume were prone to decreasing during fog. Based on previous studies, a “Crash Risk Increase Indicator (CRII)” was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risks with traffic flow characteristics. The results suggested that the proposed indicator worked well in evaluating the increase of crash risk under fog condition. It was indicated that the crash risk was prone to increase at ramp vicinities in fog conditions. Also, the average 5-min volume during fog and the lane position are important factors for crash risk increase. The differences between the regions were also explored in this study. The results indicated that the locations with heavier traffic or locations at the lanes that were closest to the median in Region 2 were more likely to observe an increase in crash risks in fog conditions. It is expected that the proposed indicator can help identify the dangerous traffic status under fog conditions and then proper ITS technologies can be implemented to enhance traffic safety when the visibility declines.

Publication Date

5-1-2018

Publication Title

Accident Analysis and Prevention

Volume

114

Number of Pages

4-11

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2017.05.004

Socpus ID

85019987788 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85019987788

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